In various technical fields there are a multitude of applications in which an accurate determination of three-dimensional (3D) coordinates of an object is required; among these technical fields are, for example, mechanical engineering, automotive industry, ceramic industry, mining industry, orthopedics, prosthetic dentistry and jewelry industry. The object to be measured can have any shape and size.
One particular application of the determination of 3D coordinates of an object involves earthwork operations, in which earthmoving machines, such as excavators with buckets or bulldozers with blades, alter the topography of a site. The progress of earthwork operations is surveyed by an optical measuring device which determines the actual 3D shape of the site and the 3D positions of buckets or blades of the earthmoving machines. Based on the repeatedly determined 3D shape representation of the site and of the 3D positions of buckets or blades, differences and deviations with a planed 3D shape representation of the site are detected and the progress of the earthwork operations is determined.
The optical measuring device utilizes a contactless sensor, such as a structured-light 3D scanner. In such a system, a projector is used to illuminate the object with a predefined pattern. A two-dimensional (2D) image of the pattern as reflected from the object is captured by a camera. The captured pattern is distorted relative to a known reference pattern. This distortion is caused by the 3D shape representation of the object. The identity of each object point can be encoded spatially (with a single pattern), temporally (with a sequence of patterns) or in a combination of both spatial and temporal encoding. The larger the number of object points, the larger is the number of image points with individual code words and, thus, the more complicate the structure of the pattern.
By illuminating the object with a sequence of temporally varying patterns such as a stripe pattern with a binary encoding or a Gray encoding, a temporal sequence of 2D images with black and white brightness values of reflected stripe patterns is captured. Brightness values of one and the same 2D image point out of the captured sequence of 2D images are measured, allowing a correlation of the 2D image point with an individual stripe which is reflected from an object point. As the individual stripe on the stripe pattern of the projector and the 2D image point of the camera are separated by a device distance, and as the projector device axis of the projected and the captured stripe patterns enclose an illumination angle, the knowledge of the device distance and the illumination angle permits a triangulation of the 3D coordinate of the correlated object point.
In WO 2008/046 663 A2 a structured-light 3D scanner of the prior art is disclosed having a projector and two photo sensor array (PSA) cameras in one common housing. The two PSA cameras have charge-coupled device (CCD) sensors with several millions of pixels. Each 2D image of the PSA cameras captures a stripe pattern as reflected from the object with a resolution of several million image points.
The PSA cameras capture the stripe pattern simultaneously but from different points of view. The optical axes of the PSA cameras have an offset angle of 5° to 20°. The PSA cameras are mounted in a rigid manner in the housing with a constant mutual distance. In accordance with the epipolar constraint, each 2D image point of a first one the two PSA cameras has a corresponding 2D image point on a known epipolar line of the second one of the two PSA cameras. With the known mutual distance and the known offset angle, the law of sines can be applied to calculate for a sequence of measured brightness values for corresponding 2D image points of the two PSA cameras the 3D coordinate of a correlated object point.
In order to accurately determine the 3D coordinates of an object in accordance with the solution described in WO 2008/046 663 A2, the object must be illuminated with a sequence of five to eleven stripe patterns. For a projector with a typical repetition rate of 30 Hz, the resulting measuring time of 3D coordinates of the object lies in the range of 160 ms and 370 ms and thus is rather slow. For a complete determination of an object which is embedded in the site, an assembly of several sequences from different points of view would be necessary. Thus, a determination of 3D coordinates of an embedded object is even more time-consuming and, in addition, requires elevated computing performance for assembly of several sequences from different points of view. This solution would be quite expensive, though.
Moreover, the intensity and nature of ambient light may influence the level of confidence of the captured 2D images. Under operation conditions, in the open and in any weather condition, ambient light may induce ambiguity in the captured 2D images, which alters the black and white brightness patterns and makes it difficult to correctly measure the brightness values and to calculate a 3D coordinate of a correlated object.
Moreover, certain shapes of an object cannot be resolved unambiguously with a 2D image even without the influence of ambient light. This ambiguity may then lead to a misinterpretation of the position or shape of a feature of the object. For instance, under certain circumstances it cannot be discovered without capturing further 2D images whether the surface of a sphere is positively or negatively arched or at which side the surface of a diagonal wall is more distant.
Thus, there is a need for a quick and low-cost determination of 3D coordinates of an object. There is also a need for a reliable determination of 3D coordinates of an object, even under the conditions of earthwork operations. Moreover, there is a need for an ambiguity-free determination of 3D coordinates of an object.